This episode of The ShiftShapers Podcast is called “Using Data To Decide When To Go Partially Self Funded” with Hari Sundram, CEO and Founder at Verikai. Today, David and Hari discuss the kinds of data that are used as a surrogate for risk.
Hari explains how brokers can inform their clients on predictive modeling and how having large data sets helps cut through anecdotal biases to provide new premiums and new outcomes.
What You’ll Learn From this Episode:
- 01:27 The right time to go self-funded
- 04:32 Data as a surrogate for risk
- 09:36 Data analysis: How brokers should inform clients
- 16:14 Predictive modeling: Creating new outcomes with repetitive data
07:45 “Ultimately, the outcome we try to look at is loss ratio applied at an individual level. That’s our goal because ultimately that’s the lynchpin of understanding whether the group has the risk necessary.”
11:19 “When you get back that score, a scenario analysis is laid out for you that says this is a score and this is how it translates into the placement of the business.”
18:24 “The only thing more dangerous than no information is a little bit of information because with a little bit of information, you’re able to apply your bias even more than with no information at all.”
21:30 “The value of predictive modeling is its ability to cut through those things that are anecdotal and find pattern that have occurred historically but have occurred historically repetitively.”
24:41 “If you want to write new premium, you need new information. What you want to do though is make sure that that new information does actually index to the outcome because the outcome is actually consistent.”